I believe the background is a relief map. Would like to see one where the color is based on the strength of support for Democrats or Republicans.

The pair of maps is extremely effective at bringing out the story about the splitting of the U.S. population. From a design standpoint, I really like it.

I love, love, love the cute annotations everywhere on the page. I imagine the designer had fun coming up with them.

Pittsburgh Puddle, Cleveland Cove, Cincinnati Slough, ...

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There is an artistic (or data journalistic) license behind the way the data are processed. Most likely, a 50% cutoff is applied to determine which map a county sits atop. The analysis is at the county level so there is neccessarily some simplification... in fact, this aggregation is needed to make the "islands" and other features contiguous.

I am a bit sad that at this moment, we are so focused on what sets us apart, and not what binds us together as a nation.

PS. Via twitter, Maciej reacted negatively to these maps: "Horribly tendentious map visualization from the NYT makes the candidate who won more votes look like a tiny minority."

This is a good illustration of selecting the chart form to bring out one's message. If the goal of the chart is to show that Clinton has more votes, I agree that these maps fail to convey that message.

What I believe the NYT designer wants to point out is that the supporters of Clinton are clustered into these densely populated urban areas, leaving the Republicans with most of the land mass. (Like I said above, because of the 50% cutoff criterion, we are over-simplifying the picture. There are definitely Democrats living somewhere in Trump's nation, and likewise Republicans residing in Clinton strongholds.)

I don't know for sure how the New York Times will present election results next week; it's going to be as hard to predict as the outcome of the election!

The Times just published a wonderful article describing all the different ways election results have been displayed in the past.

tldr; The designer has to make hard choices. Some graphics are better at one thing but worse at another. If the designer can prioritize the Qs, then the choice will come naturally. This is why the Q corner is at the top of the Trifecta framework (link).

I particularly like the non-map shown right, published in 2000.

This chart doesn't answer every question you want. But it gives a sense of how the candidates built their path to victory.

The imagery of a building works well here. The foundation of a building is its bottom, consisting of states which lean heavily to one party or the other. These foundational blocks scale with either the skew of the support or the number of electoral votes. The lower down in the building, the more solid is the bloc, which makes a lot of sense.

It's not easy to learn the exact vote totals for each state but the vertical axis is pure Tufte and sufficient for most readers.

All in all, this graphic is top-notch. It takes a little time to perfect but not too much. It has clear takeaways and I feel like I learned much more from this chart than I could in a "purple map" type of rendition.

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There is a little room for augmentation. It's how they handled the "undecided" states. For me, that is the suspense of this graphic. It's the cliffhanger.

Staring at the chart for the first time, I find that it doesn't address the question of the night: who won? Neither of the "buildings" hit the 270 level required to win the election. Also, there isn't a current vote count so readers have to figure out how many votes are required to win. That's frustrating.

There is an annotation in the middle right, explaining that three states with 37 votes have not yet issued results. That text is better placed near the peaks of the buildings next to the gap where the undecided states would eventually show up.

Also, it is interesting to expand the graphic a bit to address the question of who's likely to win and how. With three states remaining that can go either way, there are eight possible scenarios. It turns out that everything comes down to Florida. Whoever wins Florida wins the election. The other two contests don't matter! (Florida has 25 votes, New Mexico 7, Oregon 5. Gore needs 16 more votes, and Bush needs 24.)

Here is one way to present these scenarios. A little bit of hover-over effect will help here, to provide some details of each scenario.

Catching a dose of Alberto Cairo the other day. He has a good post about various Brexit/Bremain maps.

The story started with an editor of The Spectator, who went on twitter to make the claim that the map on the right is better than someone else's map on the left:

There are two levels at which we should discuss these maps: the scaling of the data, and the mapping of colors.

The raw data are percentages based on counts of voters so the scale is decimal. In general, we discretize the decimal data in order to improve comprehension. Discretizing means we lose granularity. This is often a good thing. The binary map on the left takes the discretization to its logical extreme. Every district is classified as either Brexit (> 50% in favor) or Bremain (> 50% opposed). The map on the right uses six total groups (so three subgroups of Brexit and three subgroups of Bremain.

Then we deal with mapping of numbers to colors. The difference between these two maps is the use of hues versus shades. The binary map uses two hues, which is probably most people's choice since we are representing two poles. The map on the right uses multiple shades of one hue. Alternatively, Alberto favors a "diverging" color scheme in which we use three shades of two hues.

The editor of The Spectator claims that his map is more "true to the data." In my view, his statement applies in these two senses: the higher granularity in the scaling, and also, the fact that there is only one data series ("share of vote for Brexit") and therefore only one color.

The second point relates to polarity of the scale. I wrote about this issue before - related to a satisfaction survey designed (not too well) by SurveyMonkey, one of the major online survey software services. In that case, I suggested that they use a bipolar instead of unipolar scale. I'd rather describe my mood as somewhat dissatisfied instead of a little bit satisfied.

I agree with Alberto here in favor of bipolarity. It's quite natural to underline the Brexit/Bremain divide.

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Given what I just said, why complain about the binary map?

We agree with the editor that higher granularity improves comprehension. We just don't agree on how to add graularity. Alberto tells his readers he likes the New York Times version:

This is substantively the same map as The Spectator's, except for 8 groups instead of 6, and two hues instead of one.

Curiously enough, I gave basically the same advice to the Times regarding their maps showing U.S. Presidential primary results. I noted that their use of two hues with no shades in the Democratic race obscures the fact that none of the Democratic primiaries was a winners-take-all contest. Adding shading based on delegate votes would make the map more "truthful."

That said, I don't believe that the two improvements by the Times are sufficient. Notice that the Brexit referendum is one-person, one-vote. Thus, all of the maps above have a built-in distortion as the sizes of the regions are based on (distorted) map areas, rather than populations. For instance, the area around London is heavily Bremain but appears very small on this map.

The Guardian has a cartogram (again, courtesy of Alberto's post) which addresses this problem. Note that there is a price to pay: the shape of Great Britain is barely recognizable. But the outsized influence of London is properly acknowledged.

This one has two hues and four shades. For me, it is most "truthful" because the sizes of the colored regions are properly mapped to the vote proportions.

The picture for New York is not as pretty but still intriguing. We are having a bout of summer and hence the white space (no precipitation):

Interpreting this innovative chart is a tough task - this is a given with any innovative chart. Explaining the chart requires all the text on this page.

The difficulty of interpreting the SparkRadar chart is twofold.

Firstly, the axes are unnatural. Time runs vertically, defying the horizontal convention. Also, "now" - the most recent time depicted - is at the very bottom, which tempts readers to read bottom to top, meaning we are reading time running backwards into the past. In most charts, time run left to right from past to present (at least in the left-right-centric part of the world that I live in.)

Location has been reduced to one dimension. The labels "Distance Inside" and "Distance from Storm" confuse me - perhaps those who follow weather more closely can justify the labels. Conventionally, location is shown in two dimensions.

The second difficulty is created by the inclusion of irrelevant data (aka noise). The square grid prescribes a fixed box inside which all data are depicted. In the New York graphic, something is going on in the top right corner - far away in both time and space - how does it help the reader?

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Now, contrast this chart to the more standard one, a map showing rain "clouds" moving through space.

(From Bing search result)

The standard one wins because it matches our intuition better.

Location is shown in two dimensions.

Distance from the city is shown on the map as scaled distance.

Time is shown as motion.

Speed is shown as speed of the motion. (In SparkRadar, speed is shown by the slope of imaginary lines.)

The New York Times has an eye-catching graphic illustrating the Amtrak crash last year near Philadelphia. The article is here.

The various images associated with this article vary in the amount of contextual details offered to readers.

This graphic provides an overview of the situation:

Initially, I had a fair amount of trouble deciphering this chart. I was searching hard to find the contrast between the orange (labeled RECENT TRAINS) and the red (labeled TRAIN # 188). The orange color forms a wavy area akin to a river on a map. The red line segments suggest bridges that span the river bank. The visual cues kept telling me train #188 is a typical train but that conclusion was obviously wrong.

The confusion went away after I read the next graphic:

This zoomed-in view offered some helpful annotation. The data came from three days of trains prior to the accident. Surprisingly, the orange band does not visualize a range of speeds. The width of the orange band fluctuates with the median speed over those three days. And then, the red line segments represent the speed of train #188 as it passed through specific points on the itinerary.

The key visual element to look for is the red lines exceeding the width of the orange band as train #188 rounds Frankford Junction.

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In the second graphic, the speeding is more visible. But it can be made even more prominent. For example, instead of line segments, use the same curvy element to portray the speed of train #188. Then through line width or color, emphasize train #188 and push the average train to the background.

Notice that there is an additional line snaking through the middle of the orange band. The data have been centered around this line. This type of centering is problematic: the excess speed relative to the median train has been split into halves. The reader must mentally reassemble the halves. The impact of the speeding has therefore been artificially muted.

In this next version, I keep that midpoint line and use it to indicate the median speed of the trains. Then, I show how train #188 diverged from the median speed as it neared the Junction.

This version brings out one other confusing element of the original. This line that traces the median speed is also tracing the path of the train (geographically). Actually, the line does not encode speed--it just encodes the reference level of speed. The graphic above creates an impression that train #188 "ran off track" if the reader interprets the green line as a railroad track on a map. But it is off in speed, not in physical location.

This year's U.S. primary elections have been very entertaining. Delegate maps are a handy way to keep track of the horse race. They provide data to support (or refute) the narratives created by reporters who use words like "landslide", "commanding", etc.

Here’s a delegate map used by the New York Times on the night of Mar 15th when Hillary Clinton won four out of five states, with the fifth (Missouri) being a cliffhanger:

Other media outlets are using pretty much the same form, with different color schemes.

The typical color scheme has two binary levels: one color for each candidate (NYT uses blue for Clinton, green for Sanders in the Democratic race); a lighter shade for who's leading, and a darker shade for the declared winner.

***

These maps are missing one crucial piece of information, the margin of victory. The margin is important because in most of the contests, the delegates are split proportionally.

The same shade of blue was used to describe the decisive victory in Florida (64% to 33%) and the laser-thin victory in Illinois (51% to 49%). This color scheme implies a winner-takes-all criterion.

Here is a map that includes the margin of victory, computed as the excess number of delegates won in the given state:

For the Democratic race, the narrative is that Clinton built a sizeable lead in pledged delegates in the Southern states; elsewhere, the states have been evenly split or slightly favoring Sanders (within about 10 delegates). Also, the West and Northwest have largely not spoken yet.

Other maps can be created using different measures of the margin, such as the diference in vote proportions.

Scott Klein's team at Propublica published a worthy news application, called "Hell and High Water" (link) I took some time taking in the experience. It's a project that needs room to breathe.

The setting is Houston Texas, and the subject is what happens when the next big hurricane hits the region. The reference point was Hurricane Ike and Galveston in 2008.

This image shows the depth of flooding at the height of the disaster in 2008.

The app takes readers through multiple scenarios. This next image depicts what would happen (according to simulations) if something similar to Ike plus 15 percent stronger winds hits Galveston.

One can also speculate about what might happen if the so-called "Mid Bay" solution is implemented:

This solution is estimated to cost about $3 billion.

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I am drawn to this project because the designers liberally use some things I praised in my summer talk at the Data Meets Viz conference in Germany.

Here is an example of hover-overs used to annotate text. (My mouse is on the words "Nassau Bay" at the bottom of the paragraph. Much of the Bay would be submerged at the height of this scenario.)

The design has a keen awareness of foreground/background issues. The map uses sparse static labels, indicating the most important landmarks. All other labels are hidden unless the reader hovers over specific words in the text.

I think plotting population density would have been more impactful. With the current set of labels, the perspective is focused on business and institutional impact. I think there is a missed opportunity to highlight the human impact. This can be achieved by coding population density into the map colors. I believe the colors on the map currently represent terrain.

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This is a successful interactive project. The technical feats are impressive (read more about them here). A lot of research went into the articles; huge amounts of details are included in the maps. A narrative flow was carefully constructed, and the linkage between the text and the graphics is among the best I've seen.

The counties are given shades of blue with darker shades meaning more economic distress. According to the label (Newark), the 10 red dots are the top 10 most distressed large cities in the U.S. It appears that almost all of these cities are in regions of light blue shade. That's the puzzle.

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A separate issue with this map is that it presents a static image of distress. The first paragraph of the article states: "As the most prosperous communitiesin the United States have gotten richer since the end of the Great Recession in 2009, economic conditions in many distressed areas have deteriorated even further." It would be great if we can see a before and after 2009 comparison.

On Twitter, someone pointed me to the following map of journalists who were killed between 1993 and 2015.

I wasn't sure if the person who posted this liked or disliked this graphic. We see a clear metaphor of gunshots and bloodshed. But in delivering the metaphor, a number of things are sacrificed:

the number of deaths is hard to read

the location of deaths is distorted, both in large countries (Russia) where the deaths are too concentrated, and in small countries (Philippines) where the deaths are too dispersed

despite the use of a country-level map, it is hard to learn the deaths by country

The Committee to Protect Journalists (CPJ), which publishes the data, used a more conventional choropleth map, which was reproduced and enhanced by Global Post:

They added country names and death counts via a list at the bottom. There is also now a color scale. (Note the different sets of dates.)

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In a Trifecta Checkup, I would give this effort a Type DV. While the map is competently produced, it doesn't get at the meat of the data. In addition, these raw counts of deaths do not reveal much about the level of risk experienced by journalists working in different countries.

While this is not a definitive visualization of the dataset, I use this heatmap to highlight the trouble with hiding the time dimension. Deaths are correlated with particular events that occurred at particular times.

Iraq is far and away the most dangerous but only after the Iraq War and primarily during the War and its immediate aftermath. Similarly, it is perfectly safe to work in Syria until the last few years.

A journalist can use this heatmap as a blueprint, and start annotating it with various events that are causes of heightened deaths.

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Now the real question in this dataset is the risk faced by journalists in different countries. The death counts give a rather obvious and thus not so interesting answer: more journalists are killed in war zones.

A denominator is missing. How many journalists are working in the respective countries? How many non-journalists died in the same countries?

In this graphic, the designer played with the up-is-bigger convention, drawing some loud dissent.

Begin with the question addressed by the NOAA graphic: which parts of the country has the highest likelihood of having a white Christmas? My first instinct is to look at the darkest regions, which ironically match the places with the smallest chance of snow.

Surely, the designer's idea is to play with white Christmas. But I am not liking the result.

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Then, I happen upon an older version (2012) of this map, also done by NOAA. (See this Washington Post blog for example.)

There are a number of design choices that make this version more effective.

The use of an unrelated brown color to cordon off the bottom category (0-10%) is a great idea.

Similarly, the play of hue and shade allows readers to see the data at multiple levels, first at the top level of more likely, less likely, and not likely, and then at the more detailed level of 10 categories.

Finally, there is no whiteness inside the US boundary. The top category is the lightest shade of purple, not exactly white. In the 2015 version above, the white of the snowy regions is not differentiated from the white of the Great Lakes.

I am still not convinced about the inversion of the darker-is-larger convention though. How about you?